Which performance metrics are calculated?
The analyzer computes total requests, success and error rates, average latency, p95 and p99 latency, throughput, and an error breakdown summary.
Load Test Result Analyzer
Upload load-test artifacts and get an instant metrics dashboard covering request volume, reliability, and latency distributions for release decisions.
Supports JMeter CSV, k6 JSON, and generic CSV with response time + status fields.
Export from JMeter: Listeners → Save Results. For k6: use `--out json=results.json`.
Next tool: XPath / CSS Selector Tester
About
Analyze JMeter CSV and k6 JSON results for latency, throughput, and error patterns.
Accepted input
Accepts load-test exports, timing values, HAR files, or throughput assumptions. Large result sets are summarized locally for faster inspection.
How to use
- Open Load Test Result Analyzer and paste, type, or upload the input it expects.
- Use Load sample if you want a realistic starting point before working with your own data.
- Adjust the available options until the preview or output reflects the workflow you need.
- Use copy, download, or related follow-up tools once the result looks correct.
Tips
- Pair it with other performance tools from the same category when you need a broader workflow.
- Samples load locally and can be replaced immediately with your own data.
Performance testing data is valuable only when teams can convert raw output into actionable decisions. The KalpLabs Load Test Result Analyzer helps QA and DevOps teams process JMeter CSV and k6 JSON artifacts into a concise view of service behavior under load. Instead of manually calculating latency percentiles and success ratios, engineers can upload a run file and immediately review throughput, error rates, and high-percentile response times for deployment readiness assessments.
The analyzer is practical for day-to-day release engineering. It computes total request volume, average response time, p95 and p99 latency, and response-time distribution so teams can detect whether changes impact tail performance. Error breakdown output helps isolate recurring failures by message category, improving defect triage and rollback decisions. This makes it useful for smoke performance checks, pre-production validation, and post-incident analysis where speed and clarity are critical.
Security and scale considerations are built in with file-type validation, size limits, and request throttling to prevent abusive uploads. Parsing happens server-side to keep client performance responsive even on modest devices. The resulting summary can be shared in release notes, incident timelines, and sprint retrospectives to support evidence-driven improvements. For engineering teams focused on reliability, this utility provides a lightweight but effective way to interpret load-test results with consistent methodology.
常见问题
What file formats are supported?
It supports JMeter-style CSV, k6 summary JSON, and generic CSV files containing response time and success indicators.
Can this help compare performance regressions?
Yes. By analyzing repeated runs with consistent datasets, teams can detect latency drift and error spikes after code or infrastructure changes.